Mathematics is the study of numbers and their properties. Math was developed in part through the use of abstraction and logical reasoning, from counting, calculation, measurement, and the study of the shapes and motions of physical objects.
Numerology is also the study of numbers, and the manner in which they reflect certain aptitudes and character tendencies in humans. Numbers or sequences of numbers also have certain vibrations whether they are spoken or written. Each letter for a word that spells out a number in letters provides a related vibration when spoken. Sequences of numbers provide an interrelation of vibrations. Experts in numerology use numbers to determine the best time for major moves and activities in life. Numerology is used to decide when to invest, when to marry, when to travel, when to change jobs, or relocate.
Learning is the process of acquiring knowledge or skill through study, experience or teaching. It is a process that depends on experience and leads to short-term and long-term changes in behavior potential. Learning is also a change in neural function as a consequence of experience.
The scientific conclusion that that repetition improves learning is a very old concept. In a book published in 1885 by Hermann Ebbinghaus, one the first researcher to carry out a prolonged series of experiments on human memory showed experimentally that retention of information improves as a function of the number of times the information has been studied. Since the time of Ebbinghaus, countless investigators have used repetition to study learning and memory.
Simple repetition can have a powerful impact on learning. Rote learning is learning by repetition, based on the idea that one will understand the meaning of the material the more they repeat it. Spaced repetition is a learning technique in which increasing intervals of time are used for subsequent reviews.
Repetition for learning using a pre-determined number of repetitions is also frequently used. However, the number of repetitions is used for learning are not selected based on a desired input or desired output or the beliefs of human who has a desire to learn something.
There have been many attempts to study how a human or animal brain reacts to repetitive learning techniques. For example, Russell A. Poldrack and John D. E. Gabrieli in a paper entitled “Characterizing the neural mechanisms of skill learning and repetition priming,” Brain, Vol. 124, No. 1, pp. 67-82, January 2001, teach “The changes in brain activity related to skill learning and repetition priming in a mirror-reading task were examined using functional MRI. Subjects exhibited significant learning across five training sessions and this learning generalized significantly to different spatial transformations (inverted-mirror reversed text and normal letters spelled backwards). Mirror reading, compared with reading normal text, was associated with extensive activation in occipital, temporal, parietal and frontal regions. Learning to read mirror-reversed (MR) text was associated with increased activation in left inferior temporal, striatal, left inferior prefrontal and right cerebellar regions and with decreased activity in the left hippocampus and left cerebellum. Short-term repetition priming was associated with reduced activity in many of the regions active during mirror reading and extensive item-specific practice (long-term repetition priming) resulted in a virtual elimination of activity in those regions. Short- and long-term repetition priming thus appeared to rely upon common neural mechanisms. Nearly all of the regions exhibiting significant learning-related changes also exhibited increased repetition priming effects, suggesting common neural substrates for priming and skill learning in this task. Comparison of MR items with other spatially transformed typographies showed that the learning-related changes were general to all of the spatial transformations. The results confirm the importance of striatofrontal neural networks for the acquisition of skills, and suggest that skill learning and repetition priming may have common substrates within a particular task.”
Georg Grön, David Schul, Volker Bretschneider, A. P. Wunderlich, Matthias W. Riepe in an article entitled “Alike performance during nonverbal episodic learning from diversely imprinted neural networks,” European Journal of Neuroscience 18 (11), pp. 3112-3120 (2003) teach “Performance on neuropsychological testing permits inferences to be made regarding neural networks required to solve the task. In healthy young human subjects it is common sense that differential performance in cognitive tasks results from recruitment of different neural networks and that alike performance results from recruitment of alike neural networks. It was the goal of the present study to investigate whether these assumptions are also valid in cross-cultural studies. To address this, we used functional MRI during a nonverbal episodic memory task with repeated learning. Behavioural performance in this task was alike over repeated trials in native Chinese and Caucasian subjects. Given this equivalent performance, the distinct pattern of neuronal activation observed is interpreted as the outcome of different culturally imprinted processing routines. In the ‘what’ and ‘where’ framework of visuo-spatial processing initial learning in Chinese subjects activated the dorsal stream for analysis of spatial features whereas Caucasians recruited the ventral stream for object identification. With repeated learning Chinese subjects integrated visuo-spatial processing to object coding and vice versa. Thus, imprints of culture result in activation of distinct neural networks and mandate monitoring of both behavioural performance and neural recruitment in cross-cultural studies of cognition.”
Matthew Walker and Jeffrey Ellenbogen.Many in an article entitled “Take a Walk on the Blind Side,” Focus Online, May 18, 2007, Harvard Medical School, teach we have assumed that the ability to see patterns in the welter of human experience is the product of conscious attention and thought. However we have found is that such inferential knowledge may be hatched outside the glare of consciousness, during a period of nonconscious, or offline, processing after a period of repetitive learning. Fifty-six students were asked to perform a simple inference task. Subjects who were tested shortly after an initial learning period performed, as a group, no better than chance. Groups of subjects tested after a period of at least 12 hours had a much higher success rate—nearly 80 percent of their inferences were correct. And those tested after a night's sleep were able to draw more distant connections.”
William L. Mikulas, Ph.D. professor of psychology at the University of West Florida, teaches at the Universal Resource Locator (URL) “uwf.edu/psych/faculty/mikulas.html” on the Internet teaches “many theorists have been concerned with applying principles of learning to the understanding of repetitive verbal learning and retention. Of the theories dealing with retention, the most popular for quite a while was the associative interference theory. According to this theory, retention loss is due to competition from alternative responses at the time of recall. Thus, when a person can't remember another person's name, although he once knew it, it is probably not because the name is lost from the memory storage. Rather, it is because other responses, such as other names, interfere with the retrieval of the desired name from the memory. Distinctions are often made between short-term memory (STM) and long-term memory (LTM). The basic distinction is how long the information is stored, although there is no consensus on the time something can be stored and still be considered STM.
Many theorists identify other differences between STM and LTM. Some have suggested that information in STM decays over time, while forgetting in LTM is basically a function of interference due to similarity of material. Another distinction is that STM has a limited storage capacity (how much information it can hold at one time) while LTM, for practical purposes, is not limited.”
However, none of these studies have solved all of the problems associated with repetitive learning. Therefore it is desirable to provide a method and system for automated learning through repetition.